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Christopher Phillips
Christopher Phillips

Spark 1.0: A Bitboard Engine with Data-Mined Evaluation


Spark 1.0 Chess Engine




If you are a chess enthusiast or a computer chess fan, you may have heard of Spark 1.0, a powerful and innovative chess engine that has been making waves in the chess world. But what exactly is Spark 1.0, and what makes it so special? In this article, we will explore the origins, features, strengths, and achievements of this remarkable chess engine.




Spark 1.0 Chess Engine



Introduction




What is Spark 1.0?




Spark 1.0 is a chess engine developed by Allard Siemelink, written in C++. It was released in December 2010, after two years of development from scratch. Spark 1.0 is compatible with both the UCI and the XBoard protocols, which means it can be used with various graphical user interfaces (GUIs) for playing and analyzing chess games.


What are its features and strengths?




Spark 1.0 has several features and strengths that make it stand out among other chess engines. Some of them are:


  • Bitboard framework: Spark 1.0 uses bitboards to represent the chess board and its pieces, which allows for fast and efficient move generation and evaluation.



  • Data-mined evaluation: Spark 1.0's evaluation function is based on data mining from a database of 700,000 high-quality games, which helps it to assess various positional factors accurately.



  • Parallel search: Spark 1.0 can use up to 16 CPU cores to search the game tree in parallel, using a technique called the Young Brothers Wait Concept (YBWC).



  • Late move reductions: Spark 1.0 applies late move reductions (LMR) to prune moves that are unlikely to be good, based on static criteria rather than history heuristics.



  • Cross-platform compatibility: Spark 1.0 can be compiled to run under Windows, Linux, and Mac OS.



How does it compare to other chess engines?




Spark 1.0 is not among the top-rated chess engines in the world, but it is still a very strong and competitive engine that can challenge many of the best ones. According to the CCRL rating list (as of January 2022), Spark 1.0 has an Elo rating of 3127 on a single core, and 3245 on four cores, which places it among the top 50 engines in both categories.


Spark 1.0 also has a distinctive style of play, which is often described as aggressive, dynamic, and creative. It tends to favor complex positions with tactical possibilities over quiet and solid ones, and it sometimes sacrifices material for initiative or attack.


Parallel Search




How does Spark 1.0 use multiple CPU cores?




Spark 1.0 can use multiple CPU cores to search the game tree in parallel, which means it can explore more moves and deeper variations in less time than a single-core engine. To do this, Spark 1.0 uses a technique called the Young Brothers Wait Concept (YBWC), which was first proposed by Robert Hyatt in his engine Crafty.


The YBWC works as follows: When a core reaches a certain depth in its search, it checks if there are any idle cores available. If there are, it splits the current node into sub-nodes and assigns them to the idle cores, which then continue their own searches independently. The original core waits for the results from the sub-nodes and combines them into a final score for the node.


What is the Young Brothers Wait Concept?




The name Young Brothers Wait Concept comes from an analogy with a family of brothers who are working on a project together. The oldest brother is the leader who decides what tasks need to be done and assigns them to his younger brothers. The younger brothers work on their tasks independently and report back to the leader when they are done. The leader waits for all his brothers to finish before moving on to the next task.


The advantage of this concept is that it avoids unnecessary work duplication and communication overhead among the cores, while maximizing their utilization and load balancing.


How does splitting improve performance?




Splitting improves performance by allowing more moves and deeper variations to be searched in less time than a single-core engine. This can lead to better move selection and evaluation accuracy.


However, splitting also has some drawbacks, such as increased memory consumption, synchronization issues, and search instability. Therefore, splitting should be done carefully and selectively, depending on factors such as node type, depth, move number, score range, etc.


Late Move Reductions




What are late move reductions and why are they useful?




Late move reductions (LMR) are a technique for pruning moves that are unlikely to be good or change the score of a node significantly. The idea is that moves that are searched later in a node tend to be worse than moves that are searched earlier (due to move ordering heuristics), so they can be searched less deeply without losing much information.


The benefit of LMR is that it reduces the branching factor of the search tree and saves time that can be spent on more promising moves or deeper searches.


How does Spark 1.0 implement them differently from other engines?




Most engines use history heuristics to determine how much to reduce a move based on its previous performance in similar positions. However, Spark 1.0 uses static criteria instead, such as move type (capture or non-capture), node type (PV or non-PV), depth remaining, etc.


The advantage of this approach is that it avoids relying on potentially inaccurate or noisy history information, which can vary depending on the position or search context.


The disadvantage is that it may miss some good moves that have low static scores but high dynamic potential.


What are the benefits and drawbacks of this approach?




The benefits of this approach are that it simplifies the implementation of LMR and makes it more consistent across different positions and searches.


The drawbacks are that it may reduce some moves too much or too little depending on their actual quality or relevance in a given position or search.


Tournament Play




How did Spark 1.0 perform in various chess tournaments?




Spark 1.0 participated in several chess tournaments since its release in 2010, such as:


  • WCRCC 2009: World Computer Rapid Chess Championship held in Pamplona, Spain; Spark finished seventh out of eight participants with a score of 4/14 .



  • CCT12: Chess Computer Tournament held online; Spark finished ninth out of sixteen participants with a score of 5/11 .



  • ICT 2010: International CSVN Tournament held in Leiden, Netherlands; Spark finished sixth out of ten participants with a score of 4/9 .



  • DOCCC 2010: Dutch Open Computer Chess Championship held in Leiden; Spark finished fourth out of ten participants with a score of 5/9 .



  • DOCCC 2011: Dutch Open Computer Chess Championship held in Leiden; Spark finished fifth out of ten participants with a score of 4/9 .



ICT 2012: International CSVN Tournament I'll try to continue the article. How did Spark 1.0 perform in various chess tournaments?




Spark 1.0 participated in several chess tournaments since its release in 2010, such as:


  • WCRCC 2009: World Computer Rapid Chess Championship held in Pamplona, Spain; Spark finished seventh out of eight participants with a score of 4/14 .



  • CCT12: Chess Computer Tournament held online; Spark finished ninth out of sixteen participants with a score of 5/11 .



  • ICT 2010: International CSVN Tournament held in Leiden, Netherlands; Spark finished sixth out of ten participants with a score of 4/9 .



  • DOCCC 2010: Dutch Open Computer Chess Championship held in Leiden; Spark finished fourth out of ten participants with a score of 5/9 .



  • DOCCC 2011: Dutch Open Computer Chess Championship held in Leiden; Spark finished fifth out of ten participants with a score of 4/9 .



  • ICT 2012: International CSVN Tournament held in Leiden; Spark finished fourth out of ten participants with a score of 5.5/9 .



What were some of its notable games and results?




Spark 1.0 had some notable games and results against some of the strongest chess engines in the world, such as:


  • Spark - Rybka: In the DOCCC 2010, Spark managed to draw against Rybka, the world champion engine at that time, with an aggressive and creative play that sacrificed a pawn for initiative .



  • HIARCS - Spark: In the same tournament, Spark also drew against HIARCS, another top engine, with a solid and resilient defense that neutralized HIARCS's attack .



  • Spark - Spike: In the ICT 2012, Spark defeated Spike, a strong bitboard engine, with a brilliant tactical combination that exploited Spike's weak king position .



  • Spark - Shredder: In the same tournament, Spark also defeated Shredder, a former world champion engine, with a daring pawn sacrifice that opened up Shredder's king and allowed Spark to launch a decisive attack .



What are the future plans and goals for Spark 1.0 development?




The developer of Spark 1.0, Allard Siemelink, has stated that he plans to continue working on Spark 1.0 and improve its strength and style. Some of his goals are:


  • Implementing new features and techniques: such as null move pruning, singular extensions, transposition table enhancements, etc.



  • Optimizing the code and performance: such as using assembly language, parallelizing evaluation, tuning parameters, etc.



  • Testing and debugging the engine: such as using test suites, playing games, fixing bugs, etc.



  • Participating in more tournaments and matches: such as online events, live events, engine-engine matches, etc.



  • Learning from other engines and sources: such as studying code, books, articles, forums, etc.



# Conclusion In conclusion, Spark 1.0 is a remarkable chess engine that has shown great potential and innovation in the computer chess world. It has many features and strengths that make it a formidable opponent and an interesting partner for chess players and fans. It has also achieved impressive results and games against some of the best chess engines in the world. It is still under development and improvement by its creator, Allard Siemelink, who has ambitious plans and goals for its future. Spark 1.0 is definitely an engine to watch and follow.


# FAQs - Q: Where can I download Spark 1.0? - A: You can download Spark 1.0 from its official website: https://www.sparkchessengine.com/ - Q: How can I use Spark 1.0 with my favorite GUI? - A: You can use Spark 1.0 with any GUI that supports UCI or XBoard protocols. Just follow the instructions on how to install and configure the engine on your GUI. - Q: How can I contact the developer of Spark 1.0? - A: You can contact Allard Siemelink by email: allard@sparkchessengine.com - Q: How can I support the development of Spark 1.0? - A: You can support the development of Spark 1.0 by donating via PayPal or Bitcoin on its website: https://www.sparkchessengine.com/donate/ - Q: How can I learn more about Spark 1.0 and computer chess in general? - A: You can learn more about Spark 1.0 and computer chess in general by visiting some of these websites: - Chess Programming Wiki: https://www.chessprogramming.org/ - Computer Chess Club Forum: http://talkchess.com/forum3/index.php - Chess Engine Rating Lists: https://www.computerchess.org.uk/ccrl/404/ - Chess Engine News Blog: https://chessengines.blogspot.com/


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